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An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Author

Listed:
  • Wen-chuan Wang

    (North China University of Water Resources and Electric Power)

  • Yu-jin Du

    (North China University of Water Resources and Electric Power)

  • Kwok-wing Chau

    (The Hong Kong Polytechnic University)

  • Dong-mei Xu

    (North China University of Water Resources and Electric Power)

  • Chang-jun Liu

    (China Institute of Water Resources and Hydropower Research)

  • Qiang Ma

    (China Institute of Water Resources and Hydropower Research)

Abstract

Accurate and consistent annual runoff prediction in a region is a hot topic in management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, wavelet packet decomposition (WPD) is adopted to further decompose the IMF with the maximum SE into several appropriate components. Then long short-term memory (LSTM) model, a deep learning algorithm based recurrent approach, is employed to predict all components. Finally, forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to seven annual series from different areas in China, is evaluated based on four evaluation indexes (R, MAE, MAPE and RMSE). Results indicate that ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four evaluation indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, rendering it an efficient instrument for scientific management and planning of water resources.

Suggested Citation

  • Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:14:d:10.1007_s11269-021-02920-5
    DOI: 10.1007/s11269-021-02920-5
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    References listed on IDEAS

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    Cited by:

    1. Xi Yang & Zhihe Chen & Min Qin, 2024. "Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 269-286, January.
    2. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    3. Shuai Liu & Hui Qin & Guanjun Liu & Yang Xu & Xin Zhu & Xinliang Qi, 2023. "Runoff Forecasting of Machine Learning Model Based on Selective Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4459-4473, September.
    4. Morteza Pakdaman & Iman Babaeian & Zohreh Javanshiri & Yashar Falamarzi, 2022. "European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 611-623, January.
    5. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.

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